The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), RMSprop, Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis.

翻译：巴氏涂片是一种用于早期宫颈癌诊断的筛查方法。在卷积神经网络（CNN）模型中，选择合适的优化器对于CNN在图像分类任务（包括宫颈癌巴氏涂片图像分类）中的成功至关重要。本研究使用随机梯度下降（SGD）、RMSprop、Adam、AdaGrad、AdaDelta、Adamax和Nadam优化器，对来自SipakMed数据集的宫颈癌巴氏涂片图像进行分类。本研究采用的CNN架构包括Resnet-18、Resnet-34和VGG-16，每种架构均使用了迁移学习模型。根据测试结果，我们得出结论：迁移学习模型在所有CNN架构和优化技术上均表现更优，并且在迁移学习模型中，优化器对模型训练的影响较小。Adamax优化器在VGG-16和Resnet-18架构上分别取得了72.8%和66.8%的最佳准确率。Resnet-34架构的准确率为54.0%，比Nadam优化器的结果低0.034%。总体而言，Adamax是适用于Resnet-18、Resnet-34和VGG-16架构进行宫颈癌分类的CNN优化器。本研究为巴氏涂片图像分析的CNN模型配置提供了新的见解。